Advisor: Hang Lu, Ph.D. (Georgia Institute of Technology)
Jennifer Curtis, Ph.D. (Georgia Institute of Technology)
Daniel Goldman, Ph.D. (Georgia Institute of Technology)
Patrick McGrath, Ph.D. (Georgia Institute of Technology)
Le Song, Ph.D. (Georgia Institute of Technology)
Mark Styczynski, Ph.D. (Georgia Institute of Technology)
An automated and low-cost microfluidic platform for behavioral phenotyping of Caenorhabditis elegans
Animal behavior results from a multitude of factors, including external stimuli and internal neural state, as well as past experiences. Behavior is therefore highly variable and dependent on developmental trajectory. This plays clear roles in disease, including psychiatric disorders and diseases that span a continuum, such as autism spectrum disorder. At the same time, these disorders are partially heritable, and usually this heritability is multigenic. In order to address how genes and the environment interact to regulate variability, large-scale experiments with high repeatability are requisite. Caenorhabditis elegans, a small roundworm, significantly simplifies genetic experiments through ease of isogenic culture. However, no technology currently exists that can provide continuous temporal monitoring and behavioral analysis of C. elegans for days-long timescales. In this thesis, a low-cost microscopy and analysis system will be developed to enable scalable implementation of a dynamically controlled microfluidic environment. A general-purpose behavior analysis system will be implemented to evaluate behavioral flexibility across biologically relevant physical and chemical environments. This same analysis technique will then be used to compare the behavior of a library of recombinant inbred C. elegans lines, with the goal to map quantitative behavioral traits to a set of genetic loci and begin addressing the complex question of gene-environment interactions in relation to behavior. This system will also be useful for other large-scale behavioral applications, including drug screening and associative learning.